OpenCV Python

How to Crop an Image using OpenCV and Python

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This tutorial will teach you how to crop an image with OpenCV.

What is Cropping?

Cropping, one of the most crucial operations of image processing — is the process of selecting and extracting regions of interest (or simply, ROI) of an image, excluding redundant parts not required for further processing.

Before we move ahead, excluding redundant parts of an image, if you aren’t quite familiar with act indexing and slicing with NumPy, I strongly recommend you revise an in-depth tutorial already made for you.

Without further ado, let’s begin slicing the region of interest in our image.

Project Structure

Before we get started implementing our Python script for this tutorial, let’s first review our project directory structure:

The directory contains :

  • Our Python script file
  • Our experimental image, elon_musk_tesla.png.

Let’s now implement our Python script using OpenCV!

Implementing the Image Crop Script

First, make a new script, naming it, and insert the following code:

Let’s import our required Python packages — all we need is cv2 for our OpenCV bindings and argparse for command line arguments (lines 2 – 3). Here we only need a single argument, --image, to specify where the input image is located (lines 6 – 8).

Next, we will instruct OpenCV to go and find the image "elon_musk_tesla.png", read it, and then store it in this variable "image". Then later, display the output towards our screen:

Figure 1: The original input image

Now to crop out only the body of Elon Musk from the image, we will supply a NumPy array slice to grab that region of the image, starting at (65, 1) and ending at (256, 537).

Let’s have a look at the cropped region of Elon’s body:

Figure 2: Elon’s body

To clarify the indexes supplied above, we must remember OpenCV represents NumPy arrays with:

  • First, the height of the image (Number of rows)
  • Then, the width of an image (Number of columns)

And for cropping to be done, we have to include these four indexes, which translates to:

  • Start y: The starting y-coordinate. We start the crop at y=1.
  • End y: The ending y-coordinate. We end at y=537.
  • Start x: The starting x-coordinate. That being the case, we start at x=65.
  • End x: The ending x-coordinate. We’ll end the image slice at x=256.

Similarly, we can crop only a section of his face as we’ve done with his body by supplying a NumPy array slice, starting at (124, 3) and ending at (201,80).

Take a look at the output:

Figure 3: Elon’s Face

Finally, we can extract the region which contains only the Tesla vehicle in the image, by supplying a NumPy array slice starting at (227, 212) and ending at (825,430).

Now, take a look at the output:

Figure 4: Tesla Car

Final Thoughts

Have you noticed any pattern until now?

When applying the slicing to our image, we are always following a specific syntax pattern which is:

image[startY:endY, startX:endX]

  • (startY, endY): The starting and ending (y) coordinates of an image.
  • (startX, endX): The starting and ending (x) coordinates of an image.

Makes sense?

If it feels a bit confusing for you, don’t give up yet.

I remember a couple of years back getting started within indexing with NumPy. Honestly, it wasn’t fun. However, the more you keep practicing, trust me, you’ll keep making improvements.

Display OpenCV Crop Result

Now that’s implemented, it’s time to run our script. So, fire up your terminal, and execute the following command:

The output we’ll get should match the results in the previous sections shown above.


This article has explained how to use OpenCV to slice the region of an image we are interested in and cut off unwanted regions from an image, which we aren’t required for further processing — using the syntax:

cropImage = image[startY:endY, startX:endX]

That’s it. Nothing else.

What’s Next?

Now, what’s next? in the following tutorial, we will explore the library OpenCV’s functionalities. Until then, share, like the video above, comment, and subscribe.

Further Reading

We have listed some useful resources below if you thirst for more reading.

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